Estimating road lane geometry using lane marker observations

ABSTRACT

Aspects of the disclosure relate generally to detecting the edges of lane lines. Specifically, a vehicle driving on a roadway may use a laser to collect data for the roadway. A computer may process the data received from the laser in order to extract the points which potentially reside on two lane lines defining a lane. The extracted points are used by the computer to determine a model of a left lane edge and a right lane edge for the lane. The model may be used to estimate a centerline between the two lane lines. All or some of the model and centerline estimates, may be used to maneuver a vehicle in real time and also to update or generate map information used to maneuver vehicles.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This patent application is related to U.S. patent application Ser. No.13/427,964 entitled “DETECTING LANE MARKINGS” filed concurrentlyherewith on Mar. 23, 2012, the entire disclosure of which is herebyincorporated by reference herein.

BACKGROUND

Autonomous vehicles use various computing systems to aid in thetransport of passengers from one location to another. Some autonomousvehicles may require an initial input or continuous input from anoperator, such as a pilot, driver, or passenger. Other autonomoussystems, for example autopilot systems, may be used only when the systemhas been engaged, which permits the operator to switch from a manualmode (where the operator exercises a high degree of control over themovement of the vehicle) to an autonomous mode (where the vehicleessentially drives itself) to modes that lie somewhere in between.

Such vehicles are typically equipped with various types of sensors inorder to detect objects in the surroundings. For example, autonomousvehicles may include lasers, sonar, radar, cameras, and other deviceswhich scan and record data from the vehicle's surroundings. Sensor datafrom one or more of these devices may be used to detect objects andtheir respective characteristics (position, shape, heading, speed,etc.). This detection and identification is a critical function for thesafe operation of autonomous vehicle.

In some autonomous driving systems, features such as lane markers areignored by the autonomous driving system. When the lane markers areignored, the autonomous vehicle may maneuver itself by relying moreheavily on map information and geographic location estimates. This maybe less useful in areas where the map information is unavailable,incomplete, or inaccurate.

Some non-real time systems, such as those systems which do not need toprocess the information and make driving decisions in real time, may usecameras to identify lane markers. For example, map makers may use cameraimages to identify lane lines. This may involve processing images inorder to detect visual road markings such as painted lane boundaries inone or more camera images. However, the quality of camera images isdependent upon the lighting conditions when the image is captured. Inaddition, the camera images must be projected onto the ground orcompared to other images in order to determine the geographic locationof objects in the image.

BRIEF SUMMARY

One aspect of the disclosure provides a method. The method includesaccessing sensor data collected for a roadway. The sensor data includesdata points having location information for objects. The method alsoincludes determining an initial estimate for a location of an edge of alane marker bounding a lane; generating a set of possible estimates forthe location of the edge based on an offset distance; for each estimateof the set of possible estimates, determining a likelihood valueindicative of how likely the estimate is to be the location of the edge;and selecting a most likely estimate of the set of possible estimatesbased on the likelihood values. A processor fits the sensor data pointsto a model describing a location and shape of the edge. The method alsoincludes determining a representation of the edge based on the model.

In one example, determining the initial estimate includes accessingdetailed map information for the roadway, and the initial estimate isbased on a location of an edge included in the detailed map information.In another example, determining the initial estimate is based on aprevious representation of the location of the edge of the lane marker.In yet another example, generating the set of possible estimates for thelocation of the edge includes moving the initial estimate laterally fromthe initial estimate the offset distance a plurality of times. In afurther example, the method includes using the representation of theedge to maneuver a vehicle autonomously. In one example, the method alsoincludes receiving the sensor data from a laser device. In anotherexample, the method also includes filtering the sensor data to identifydata points within a pre-determined distance of the selected most likelyestimate and the data points fit to the model describing the locationand shape of the edge are the identified data points.

In another example, the method also includes determining an initialestimate for a location of a second edge of a second lane markerbounding the lane; generating a second set of possible estimates for thelocation of the second edge based on the offset distance; for eachestimate of the second set of possible estimates, determining a secondlikelihood value indicative of how likely the estimate is to be thelocation of the second edge; selecting a second most likely estimate ofthe second set of possible estimates based on the likelihood values;filtering the sensor data to identify second data points within thepre-determined distance of the selected second most likely estimate;fitting the identified second data points to a second model describing asecond location and shape of the second edge; and determining a secondrepresentation of the second edge based on the second model. In thisexample, the method may also include determining a centerline of thelane based on the representation and the second representation. Themethod may also include maneuvering a vehicle autonomously based on therepresentation and the second representation.

Another aspect of the disclosure provides a device. The device includesmemory storing sensor data collected for a roadway. The sensor dataincludes data points having location information for objects. The devicealso includes a processor coupled to the memory. The processor isconfigured to determine an initial estimate for a location of an edge ofa lane marker bounding a lane; generate a set of possible estimates forthe location of the edge based on an offset distance; for each estimateof the set of possible estimates, determine a likelihood valueindicative of how likely the estimate is to be the location of the edge;select a most likely estimate of the set of possible estimates based onthe likelihood values; fit the sensor data points to a model describinga location and shape of the edge; and determine a representation of theedge based on the model.

In one example, the processor is configured to determine the initialestimate by accessing detailed map information for the roadway, and theinitial estimate is based on a location of an edge included in thedetailed map information. In another example, the processor isconfigured to determine the initial estimate based on a previousrepresentation of the location of the edge of the lane marker. In yetanother example, the processor is configured to generate the set ofpossible estimates for the location of the edge by moving the initialestimate laterally from the initial estimate the offset distance aplurality of times. In a further example, the processor is configured touse the representation of the edge to maneuver a vehicle autonomously.In another example, the processor is configured to receive the sensordata from a laser device. In yet another example, the processor isconfigured to filter the sensor data to identify data points within apre-determined distance of the selected most likely estimate and thedata points fit to the model describing the location and shape of theedge are the identified data points.

In another example, the processor is configured to determine an initialestimate for a location of a second edge of a second lane markerbounding the lane; generate a second set of possible estimates for thelocation of the second edge based on the offset distance; for eachestimate of the second set of possible estimates, determine a secondlikelihood value indicative of how likely the estimate is to be thelocation of the second edge; select a second most likely estimate of thesecond set of possible estimates based on the likelihood values; filterthe sensor data to identify second data points within the pre-determineddistance of the selected second most likely estimate; fit the identifiedsecond data points to a second model describing a second location andshape of the second edge; and determine a second representation of thesecond edge based on the second model. In this example, the processormay also be configured to determine a centerline of the lane based onthe representation and the second representation and to maneuver thevehicle along the centerline. The processor may also be configured tomaneuver the vehicle autonomously based on the representation and thesecond representation.

A further aspect of the disclosure provides a tangible computer-readablestorage medium on which computer readable instructions of a program arestored. The instructions, when executed by a processor, cause theprocessor to perform a method. The method includes accessing sensor datafor a roadway, the sensor data includes data points having locationinformation for objects; determining an initial estimate for a locationof an edge of a lane marker bounding a lane; generating a set ofpossible estimates for the location of the edge based on an offsetdistance; for each estimate of the set of possible estimates,determining a likelihood value indicative of how likely the estimate isto be the location of the edge; selecting a most likely estimate of theset of possible estimates based on the likelihood values; fitting thesensor data points to a model describing a location and shape of theedge; and determining a representation of the edge based on the model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional diagram of a system in accordance with aspects ofthe disclosure.

FIG. 2 is an interior of an autonomous vehicle in accordance withaspects of the disclosure.

FIG. 3A is an exterior of an autonomous vehicle in accordance withaspects of the disclosure.

FIG. 3B is a pictorial diagram of a system in accordance with aspects ofthe disclosure.

FIG. 3C is a functional diagram of a system in accordance with aspectsof the disclosure.

FIG. 4 is a diagram of map information in accordance with aspects of thedisclosure.

FIG. 5 is a diagram of laser data in accordance with an implementation.

FIG. 6 is another diagram of laser data in accordance with animplementation.

FIG. 7 is a diagram of a roadway in accordance with an implementation.

FIG. 8 is a diagram of laser data and map information in accordance withaspects of the disclosure.

FIG. 9 is another diagram of example laser data and map information inaccordance with aspects of the disclosure.

FIG. 10 is a further diagram of example laser data and map informationin accordance with aspects of the disclosure.

FIG. 11 is a flow diagram in accordance with aspects of the disclosure.

DETAILED DESCRIPTION

In one aspect of the disclosure, laser scan data including data pointsmay be collected along a roadway using one or more lasers. The datapoints may describe intensity and location information for the objectsfrom which the laser light was reflected. Using the laser data, aninitial estimate for the location of an edge of a lane line bounding alane may be determined. The initial estimate may be determined based ondetailed map information, previous estimates, and/or other methods.

The initial estimate may be used to generate a set of possible estimatesfor the location of the edge based on an offset distance. For eachestimate of the set, a likelihood value indicative of how likely theestimate may be to the location of the edge may be determined. The mostlikely estimate may be selected based on the likelihood values.

The laser scan data may be then filtered to identify data points withina pre-determined distance of the selected most likely estimate. Thefiltering may also include using a lane line detection algorithm. Theidentified data points may be fit to a model describing the location andshape of the edge. A representation of the edge may be then determinedbased on the model. These steps may be repeated or performedsimultaneously to in order to determine representations of other theedges of other lane lines.

As shown in FIG. 1, an autonomous driving system 100 in accordance withone aspect of the disclosure includes a vehicle 101 with variouscomponents. While certain aspects of the disclosure are particularlyuseful in connection with specific types of vehicles, the vehicle may beany type of vehicle including, but not limited to, cars, trucks,motorcycles, busses, boats, airplanes, helicopters, lawnmowers,recreational vehicles, amusement park vehicles, trams, golf carts,trains, and trolleys. The vehicle may have one or more computers, suchas computer 110 containing a processor 120, memory 130 and othercomponents typically present in general purpose computers.

The memory 130 stores information accessible by processor 120, includinginstructions 132 and data 134 that may be executed or otherwise used bythe processor 120. The memory 130 may be of any type capable of storinginformation accessible by the processor, including a computer-readablemedium, or other medium that stores data that may be read with the aidof an electronic device, such as a hard-drive, memory card, ROM, RAM,DVD or other optical disks, as well as other write-capable and read-onlymemories. Systems and methods may include different combinations of theforegoing, whereby different portions of the instructions and data arestored on different types of media.

The instructions 132 may be any set of instructions to be executeddirectly (such as machine code) or indirectly (such as scripts) by theprocessor. For example, the instructions may be stored as computer codeon the computer-readable medium. In that regard, the terms“instructions” and “programs” may be used interchangeably herein. Theinstructions may be stored in object code format for direct processingby the processor, or in any other computer language including scripts orcollections of independent source code modules that are interpreted ondemand or compiled in advance. Functions, methods and routines of theinstructions are explained in more detail below.

The data 134 may be retrieved, stored or modified by processor 120 inaccordance with the instructions 132. For instance, although the systemand method is not limited by any particular data structure, the data maybe stored in computer registers, in a relational database as a tablehaving a plurality of different fields and records, XML documents orflat files. The data may also be formatted in any computer-readableformat. By further way of example only, image data may be stored asbitmaps comprised of grids of pixels that are stored in accordance withformats that are compressed or uncompressed, lossless (e.g., BMP) orlossy (e.g., JPEG), and bitmap or vector-based (e.g., SVG), as well ascomputer instructions for drawing graphics. The data may comprise anyinformation sufficient to identify the relevant information, such asnumbers, descriptive text, proprietary codes, references to data storedin other areas of the same memory or different memories (including othernetwork locations) or information that is used by a function tocalculate the relevant data.

The processor 120 may be any conventional processor, such ascommercially available CPUs. Alternatively, the processor may be adedicated device such as an ASIC. Although FIG. 1 functionallyillustrates the processor, memory, and other elements of computer 110 asbeing within the same block, it will be understood that the processorand memory may actually comprise multiple processors and memories thatmay or may not be stored within the same physical housing. For example,memory may be a hard drive or other storage media located in a housingdifferent from that of computer 110. Accordingly, references to aprocessor or computer will be understood to include references to acollection of processors or computers or memories that may or may notoperate in parallel. Rather than using a single processor to perform thesteps described herein some of the components, such as steeringcomponents and deceleration components, may each have their ownprocessor that only performs calculations related to the component'sspecific function.

In various aspects described herein, the processor may be locatedremotely from the vehicle and communicate with the vehicle wirelessly.In other aspects, some of the processes described herein are executed ona processor disposed within the vehicle while others are executed by aremote processor, including taking the steps necessary to execute asingle maneuver.

Computer 110 may include all of the components normally used inconnection with a computer such as a central processing unit (CPU),memory (e.g., RAM and internal hard drives) storing data 134 andinstructions such as a web browser, an electronic display 142 (e.g., amonitor having a screen, a small LCD touch-screen or any otherelectrical device that is operable to display information), user input140 (e.g., a mouse, keyboard, touch screen and/or microphone), as wellas various sensors (e.g., a video camera) for gathering the explicit(e.g., a gesture) or implicit (e.g., “the person is asleep”) informationabout the states and desires of a person.

In one example, computer 110 may be an autonomous driving computingsystem incorporated into vehicle 101. FIG. 2 depicts an exemplary designof the interior of an autonomous vehicle. The autonomous vehicle mayinclude all of the features of a non-autonomous vehicle, for example: asteering apparatus, such as steering wheel 210; a navigation displayapparatus, such as navigation display 215; and a gear selectorapparatus, such as gear shifter 220. The vehicle may also have varioususer input devices, such as gear shifter 220, touch screen 217, orbutton inputs 219, for activating or deactivating one or more autonomousdriving modes and for enabling a driver or passenger 290 to provideinformation, such as a navigation destination, to the autonomous drivingcomputer 110.

Vehicle 101 may also include one or more additional displays. Forexample, the vehicle may include a display 225 for displayinginformation regarding the status of the autonomous vehicle or itscomputer. In another example, the vehicle may include a statusindicating apparatus such as status bar 230, to indicate the currentstatus of vehicle 101. In the example of FIG. 2, status bar 230 displays“D” and “2 mph” indicating that the vehicle is presently in drive modeand is moving at 2 miles per hour. In that regard, the vehicle maydisplay text on an electronic display, illuminate portions of vehicle101, such as steering wheel 210, or provide various other types ofindications.

The autonomous driving computing system may capable of communicatingwith various components of the vehicle. For example, returning to FIG.1, computer 110 may be in communication with the vehicle's conventionalcentral processor 160 and may send and receive information from thevarious systems of vehicle 101, for example the braking 180,acceleration 182, signaling 184, and navigation 186 systems in order tocontrol the movement, speed, etc., of vehicle 101. In addition, whenengaged, computer 110 may control some or all of these functions ofvehicle 101 and thus be fully or merely partially autonomous. It will beunderstood that although various systems and computer 110 are shownwithin vehicle 101, these elements may be external to vehicle 101 orphysically separated by large distances.

The vehicle may also include a geographic position component 144 incommunication with computer 110 for determining the geographic locationof the device. For example, the position component may include a GPSreceiver to determine the device's latitude, longitude and/or altitudeposition. Other location systems such as laser-based localizationsystems, inertial-aided GPS, or camera-based localization may also beused to identify the location of the vehicle. The location of thevehicle may include an absolute geographical location, such as latitude,longitude, and altitude as well as relative location information, suchas location relative to other cars immediately around it which can oftenbe determined with less noise that absolute geographical location.

The vehicle may also include other features in communication withcomputer 110, such as an accelerometer, gyroscope or anotherdirection/speed detection device 146 to determine the direction andspeed of the vehicle or changes thereto. By way of example only, device146 may determine its pitch, yaw or roll (or changes thereto) relativeto the direction of gravity or a plane perpendicular thereto. The devicemay also track increases or decreases in speed and the direction of suchchanges. The device's provision of location and orientation data as setforth herein may be provided automatically to the user, computer 110,other computers and combinations of the foregoing.

The computer may control the direction and speed of the vehicle bycontrolling various components. By way of example, if the vehicle isoperating in a completely autonomous mode, computer 110 may cause thevehicle to accelerate (e.g., by increasing fuel or other energy providedto the engine), decelerate (e.g., by decreasing the fuel supplied to theengine or by applying brakes) and change direction (e.g., by turning thefront two wheels).

The vehicle may also include components for detecting objects externalto the vehicle such as other vehicles, obstacles in the roadway, trafficsignals, signs, trees, etc. The detection system may include lasers,sonar, radar, cameras or any other detection devices which record datawhich may be processed by computer 110. For example, if the vehicle is asmall passenger vehicle, the car may include a laser mounted on the roofor other convenient location. As shown in FIG. 3A, vehicle 101 maycomprise a small passenger vehicle. In this example, vehicle 101 sensorsmay include lasers 310 and 311, mounted on the front and top of thevehicle, respectively. The lasers may include commercially availablelasers such as the Velodyne HDL-64 or other models. The lasers mayinclude more than one laser beam; for example, a Velodyne HDL-64 lasermay include 64 beams. In one example, the beams of laser 310 may have arange of 150 meters, a thirty degree vertical field of view, and athirty degree horizontal field of view. The beams of laser 311 may havea range of 50-80 meters, a thirty degree vertical field of view, and a360 degree horizontal field of view. It will be understood that otherlasers having different ranges and configurations may also be used. Thelasers may provide the vehicle with range and intensity informationwhich the computer may use to identify the location and distance ofvarious objects in the vehicles surroundings. In one aspect, the lasermay measure the distance between the vehicle and the object surfacesfacing the vehicle by spinning on its axis and changing its pitch.

The aforementioned sensors may allow the vehicle to understand andpotentially respond to its environment in order to maximize safety forpassengers as well as objects or people in the environment. It will beunderstood that the vehicle types, number and type of sensors, thesensor locations, the sensor fields of view, and the sensors' sensorfields are merely exemplary. Various other configurations may also beutilized.

In addition to the sensors described above, the computer may also useinput from sensors typical non-autonomous vehicles. For example, thesesensors may include tire pressure sensors, engine temperature sensors,brake heat sensors, brake pad status sensors, tire tread sensors, fuelsensors, oil level and quality sensors, air quality sensors (fordetecting temperature, humidity, or particulates in the air), etc.

Many of these sensors provide data that is processed by the computer inreal-time, that is, the sensors may continuously update their output toreflect the environment being sensed at or over a range of time, andcontinuously or as-demanded provide that updated output to the computerso that the computer can determine whether the vehicle's then-currentdirection or speed should be modified in response to the sensedenvironment.

In addition to processing data provided by the various sensors, thecomputer may rely on environmental data that was obtained at a previouspoint in time and is expected to persist regardless of the vehicle'spresence in the environment. For example, returning to FIG. 1, data 134may include detailed map information 136, e.g., highly detailed mapsidentifying the shape and elevation of roadways, intersections,crosswalks, speed limits, traffic signals, buildings, signs, real timetraffic information, or other such objects and information.

The detailed map information 136 may also include lane markerinformation identifying the location, elevation, and shape of lanemarkers. The lane markers may include features such as solid or brokendouble or single lane lines, solid or broken lane lines, reflectors,etc. A given lane may be associated with left and right lane lines orother lane markers that define the boundary of the lane. Thus, mostlanes may be bounded by a left edge of one lane line and a right edge ofanother lane line.

FIG. 4 depicts a detailed map 400 including the same example section ofroadway (as well as information outside of the range of the laser). Thedetailed map of the section of roadway includes information such assolid lane line 410, broken lane lines 420, 440, and double solid lanelines 430. These lane lines define lanes 450 and 460. Each lane isassociated with a rail 455, 465 which indicates the direction in which avehicle should generally travel in the respective lane. For example, avehicle may follow rail 465 when driving along lane 460. In thisexample, lane 450 is bounded by a right lane line 410 and a left laneline 420, and lane 460 is bounded by a right lane line 420 and a leftlane line 430. The edges for lane 450 are edges 470, 472 while the edgesfor lane 460 are edges 474, 476.

Again, although the detailed map information is depicted herein as animage-based map, the map information need not be entirely image based(for example, raster). For example, the detailed map information mayinclude one or more roadgraphs or graph networks of information such asroads, lanes, intersections, and the connections between these features.Each feature may be stored as graph data and may be associated withinformation such as a geographic location and whether or not it islinked to other related features, for example, a stop sign may be linkedto a road and an intersection, etc. In some examples, the associateddata may include grid-based indices of a roadgraph to allow forefficient lookup of certain roadgraph features.

Computer 110 may also receive or transfer information to and from othercomputers. For example, the map information stored by computer 110 maybe received or transferred from other computers and/or the sensor datacollected from the sensors of vehicle 101 may be transferred to anothercomputer for processing as described herein. As shown in FIGS. 3B and3C, data from computer 110 may be transmitted via a network to computer320 for further processing. The network, and intervening nodes, maycomprise various configurations and protocols including the Internet,World Wide Web, intranets, virtual private networks, wide area networks,local networks, private networks using communication protocolsproprietary to one or more companies, Ethernet, WiFi and HTTP, andvarious combinations of the foregoing. Such communication may befacilitated by any device capable of transmitting data to and from othercomputers, such as modems and wireless interfaces. In another example,data may be transferred by storing it on memory which may be accessed byor connected to computers 110 and 320.

In one example, computer 320 may comprise a server having a plurality ofcomputers, e.g., a load balanced server farm, that exchange informationwith different nodes of a network for the purpose of receiving,processing and transmitting the data from computer 110. The server maybe configured similarly to the computer 110, with a processor 330,memory 350, instructions 360, and data 370.

Returning to FIG. 1, instructions 132 may include one or more lanedetection algorithms 138. For example, a lane detection algorithm mayprocess laser scan data describing the intensity and locationinformation of objects within range of the laser in order to produce aset of lane marker data points. Each beam of the laser may be associatedwith a respective subset of data points. For a single beam, the subsetof data points may be further divided into sections. For each section,the average intensity and standard deviation may be used to determine athreshold intensity. A set of lane marker data points may be generatedby comparing the intensity of each data point to the threshold intensityfor the section in which the data point appears and based on theelevation of the data point relative to the roadway surface. Thisalgorithm may also be stored at the vehicle 101, computer 320 or both.

In addition to the operations described above and illustrated in thefigures, various operations will now be described. It should beunderstood that the following operations do not have to be performed inthe precise order described below. Rather, various steps can be handledin a different order or simultaneously, and steps may also be added oromitted.

A vehicle including one or more lasers may be driven along a roadway.For example, the laser may be an off board sensor attached to a typicalvehicle or a part of an autonomous driving system, such as vehicle 101.FIG. 5 depicts vehicle 101 on a section of the roadway 500 correspondingto the detailed map information of FIG. 4. In this example, the roadwayincludes solid lane line 510, broken lane lines 520 and 540, double lanelines 530, and lanes 550 and 560.

As the vehicle's laser or lasers are moved along, the vehicle maycollect data points including range and intensity information for thesame location (point or area) from several directions and/or atdifferent times. For example, each data point may include an intensityvalue indicative of the reflectivity of the object from which the lightwas received by the laser as well as location information.

For example, the laser scan data may be processed by computer 110 (orcomputer 320) to generate geographic location coordinates. Thesegeographic location coordinates may include GPS latitude and longitudecoordinates (x,y) with an elevation component (z), or may be associatedwith other coordinate systems. The result of this processing is a set ofdata point. Each data point of this set may include an intensity valueindicative of the reflectivity of the object from which the light wasreceived by the laser as well as location information: (x,y,z).

FIG. 6 depicts an exemplary image 600 of vehicle 101 approaching anintersection. The image was generated from laser scan data collected bythe vehicle's lasers for a single 360 degree scan of the vehicle'ssurroundings, for example, using the data points of all of the beams ofthe collecting laser(s). The white lines represent how the laser “sees”its surroundings. When the data points of a plurality of beams areconsidered together, the data points may indicate the shape andthree-dimensional (3D) location (x,y,z) of other items in the vehicle'ssurroundings. For example, the laser scan data may indicate the outline,shape and distance from vehicle 101 of various objects such as people610, vehicles 620, and curb 630.

FIG. 7 depicts another example 700 of laser scan data collected for asingle scan while a vehicle is driven along roadway 500 of FIG. 5 (andalso that depicted in map information 400 of FIG. 4). In the example ofFIG. 7, vehicle 101 is depicted surrounded by laser lines 730 indicatingthe area around the vehicle scanned by the laser. Each laser line mayrepresent a series of discrete data points from a single beam. Datapoints from more highly reflective features such as lane lines, whitepaint, reflectors, or those with retroreflective properties may have agreater intensity than less reflective features. In this example,reference line 720 connects the data points 710 associated with a solidlane line and is not part of the laser data.

FIG. 7 also includes data points 740 generated from light reflecting offof the double lane lines 530 (shown in FIG. 5) as well as distance anddata points 750 generated form light reflecting off of the broken laneline 520 (shown in FIG. 5). In addition to features of the roadway 500,the laser scan data of FIG. 7 may include data from other objects suchas 760 generated from another object in the roadway, such as a vehicle.

FIG. 8 is a comparison 800 of the map 400 of FIG. 4 with the laser scandata of FIG. 7. In this example, data points 740 are associated withdouble lane lines 730, and data points 750 are associated with lane line720. However, it can be seen that at least some of the laser scan datadoes not line up perfectly with the map information 400.

The computer may determine an initial estimate for the location of anedge of a lane lines. For example, returning to FIG. 5, if vehicle 101is being driven in lane 560, the relevant edges for this lane may bethose associated with double lane lines 530 and lane line 520, or edges574 and 576. Thus, the computer may generate an initial estimate foredge 574 and/or edge 576. The following examples are related to a singleedge 574 for a single lane line, though these steps may be repeated forother edges as well. In one example, a previous estimate of the locationof the same or a different edge of the same lane line may be used as aninitial estimate. In another example, the computer may first cluster thelane marker data points, extract a general shape and use this as aninitial estimate. An initial estimate for the lane edges may also bemade by observing where vehicles (including the vehicle with the laseror lasers and/or other vehicles) have driven or where they currentlyare. In this example, the heading of other vehicles may also be used asan initial estimate for the lane direction. An initial estimate may alsobe randomly generated, for example using a random sample consensus(RANSAC) algorithm.

In yet another example, the computer may access the detailed mapinformation and use it to determine an initial estimate. In thisexample, the pertinent section of the detailed map may be identifiedbased on the location of the vehicle (or laser) when the laser scan datawas collected determined from various geographic positioning devicessuch as the geographic positioning component 144. FIG. 9 is a detailedview of a section of data points 750 and lane line 420. In this example,data points 950 may be associated with a black or darker area of theroadway, while data points 750 may be associated with a lane line. Aslane lines are more reflective than a black roadway, data points 950have a lower intensity value than data points 750 and are thus depictedas grey (rather than black). In this example, the location of edge 474may be used as an initial estimate 910 for the location of edge 574.

Using the initial estimate, the computer may generate additionalpossible estimates. For example, a set of possible locations for an edgeof a lane line may be generated by offsetting the initial estimate tothe left and to the right (relative to the lane line). The offset may bea relatively small distance, for example, a centimeter or less, and maybe repeated in each direction for a much larger distance, for example, 1meter. FIG. 10 is an example of 6 offsets 1001-1006, 3 to the right and3 to the left of the initial estimate 910. Each of these offsets isseparated by an offset distance of Δd. The offset distance may beconsistent between each of the estimates or it may differ.

Each of particular estimate of the set of estimates may be evaluated todetermine how likely the particular estimate is to be the actual laneline edge. Various criteria may be utilized in this determination. Forexample, if available, a lane line detection algorithm, such as lanedetection algorithms 138, may be used to extract lane marker data pointsor data points that are potentially residing on lane lines. In thisexample, the criteria may include the total number of lane marker datapoints within a distance (e.g., on the order of 25 to 40 centimeters),from each estimate of the set and/or the dispersion of the lane markerdata points close (e.g., on the order of 25 to 40 centimeters) to theestimate (in particular how well lane marker data point are spread overthe entire range of the estimate). The dispersion may be calculated inmany ways. For example, the distance between each point in the estimateand some reference point (e.g., the first point in the estimate) couldbe calculated and the standard deviation of this distance could be used.The higher the standard deviation, the better the spread of the pointsalong the estimate. However, any measure for approximating how well thepoints are spread out along the estimate would be useful.

The criteria may also include the total number of data points (all datapoints, and not specifically lane marker data points) within thedistance of the estimate, and, if available, the deviation of theestimate from the most recent estimate.

Based on the aforementioned criteria, the computer may select theestimate most likely to be the lane line edge. For example, all or someof the aforementioned criteria may be used to determine a likelihoodvalue for each estimate of the set of estimates. The estimate with thehighest likelihood value may be selected as the most likely estimate.The computer may then identify a set of data points by filtering all ofthe data points or the lane marker data points which are within adistance of the selected estimate. Again the filter may be on the orderof 25 to 40 centimeters.

The identified set of data points may be fit to a model describing thelocation and shape of the edge. For example, the fit may be based onparametric models such linear, cubic, higher-order polynomials, splines,or clothoids, or non-parametric models. The fit may also involve using aleast squares method and a quadratic model. In this example, to improvethe robustness of the parametric model, an independent variable used forthe model may first be aligned with a heading or principle direction forthe estimate. For example, for edge 474, the direction of rail 465 (orsome small deviation from the direction) for lane 460 may be thedirection of the estimate.

In some examples, the model may be post-processed to remove any noisysections. This may be useful at end points (between broken sections of alane line or simply the end of a lane line) where there are only fewdata points have a significant bearing on the solution. The computer mayanalyze the number points used to calculate a particular part of theestimate (the support) over various sections of the model. This mayinclude comparing the absolute number of data points of the identifiedset of data points in a given section to the number of data points ofthe identified set of data points of other second or over the entiremodel. The analysis may also include evaluating the curvature orsmoothness of sections and filtering or smoothing out those sectionsthat appear spurious. For example, some end sections can be removed orother sections can be smoothed so that the sections deviate less fromthe adjacent sections. In this regard, if an end section of the estimatedoes not have enough support, it may be removed from the estimate, sothat the estimate does not extend as far as it originally did.

The post-processing may also include comparing the model to the previousestimate. For example, the computer may determine how much the previousestimate and the current estimate for a lane edge differ. The computermay allow for some maximum deviation or difference (e.g., it may beunlikely that the lane has completely changed direction). If the currentestimate differs more than this maximum, the entire estimate, or anysection of the estimate that differs for than the maximum, may be thrownout or ignored.

Although the above examples relate to estimating the location of asingle edge of a lane line, the estimate of another edge or other edgesmay be computed simultaneously or in sequence using the aspectsdescribed below. In addition, the aspects above may be applied to theedges of other types of lane markers, such as reflectors. The computermay also calculate all or less than all of the edges proximate to thevehicle (for example within the line of sight of the laser or lasers).This may be useful where the laser scan data is being used to generatemap information. If the vehicle is driving autonomously, the computermay estimate locations for all of the edges or only the locations ofthose edges of the lane in which the vehicle is traveling. For example,returning to FIG. 5, vehicle 101 is driving in lane 560. Thus, computer110 may estimate a model of the location of edges 574 and 576. Thecomputer may also estimate a model of the locations of other edges suchas edges 570 and 572.

The resulting models may then describe the left and right edges of therelevant lane. The models may be used to infer the centerline. Thecomputer may use the centerline in order to maneuver the vehicle. Forexample, the centerline may be used as a reference position for wherethe vehicle should be driven.

The models may also be used to extract continuous representations of theedges (for example, along a length of a lane line) or any other type ofrepresentation desired (for example, points, line segments, etc). Asnoted above, the representations may also be used in futuredeterminations, for example, as the initial estimates, to determine thelikelihood that an estimate of the location of an edge is the actualedge, or for comparison purposes if the models are post-processed asdescribed above.

Flow diagram 1100 of FIG. 11 depicts some of the aspects describedabove. Each of the following steps may be performed by computer 110,computer 320, or a combination of both. In this example, laser scan dataincluding data points is collected along a roadway using one or morelasers or other devices at block 1102. As noted above, the data pointsmay describe intensity and location information for the objects fromwhich the laser light was reflected. Using the laser data, an initialestimate for the location of an edge of a lane line bounding a lane isdetermined at block 1104. The initial estimate may be determined basedon detailed map information, previous estimates, and/or any of theexamples described above.

The initial estimate is used to generate a set of possible estimates forthe location of the edge based on an offset distance at block 1106. Foreach estimate of the set, a likelihood value indicative of how likelythe estimate is to be the location of the edge is determined at block1108. The most likely estimate is selected based on the likelihoodvalues at block 1110.

The laser scan data is then filtered to identify data points within apre-determined distance of the selected most likely estimate at block1112. The filtering may also include using a lane line detectionalgorithm. The identified data points are fit to a model describing thelocation and shape of the edge at block 1114. A representation of theedge is then determined based on the model at block 1116. As describedabove, these steps may be repeated or performed simultaneously to inorder to determine representations of the edges of other lane lines.

The resulting representations can then be used by a computer, such ascomputer 110, to maneuver an autonomous vehicle, such as vehicle 101, inreal time. For example, returning to the example of FIG. 5, the computer110 may use the representations to keep vehicle 101 in lane 560 betweenlane lines 520 and 530. As the vehicle moves along the lane, thecomputer 110 may continue to process the laser data repeating all orsome of the steps described above. The models may also be useful forpassive or active safety systems in non-autonomous or semi-autonomousvehicles.

In some examples, the edge modeling may be performed at a later time byanother computer. For example, the laser scan data may be uploaded ortransmitted to computer 320 for processing. The modeling may beperformed as described above, and the resulting models may be used togenerate, update, or supplement the map information used to maneuver theautonomous vehicles. Similarly, this information may be incorporatedinto maps used for navigation (for example, GPS navigation) and otherpurposes.

If a lane line detection algorithm is used, the results need not be 3D(x,y,z) data points. Rather this data may be expressed as line segments,image coordinates, or noisy estimates, and may be used in place of thelane marker data points described above. For example, data from sensorsother than a laser may also be analyzed using the concepts discussedabove.

The aspects described above may provide additional benefits. Forexample, by filtering the data points to examine only those relevant tothe most likely estimate of an edge before the fitting, the amount ofdata to be processed and fit to a model may be dramatically reduced.This, in turn, may reduce the processing power cost required to fit themodel. If the laser scan data is being processed in real time toidentify the location of lane line edges to maneuver an autonomousvehicle, the modeling may be performed every tenth of a second. This isimportant because the vehicle's computer must be able to make decisionsquickly. Thus, the value of the savings in terms of time and processingpower cost may be enormous.

As these and other variations and combinations of the features discussedabove can be utilized without departing from the subject matter asdefined by the claims, the foregoing description of exemplaryimplementations should be taken by way of illustration rather than byway of limitation of the subject matter as defined by the claims. Itwill also be understood that the provision of the examples describedherein (as well as clauses phrased as “such as,” “e.g.”, “including” andthe like) should not be interpreted as limiting the claimed subjectmatter to the specific examples; rather, the examples are intended toillustrate only some of many possible aspects.

The invention claimed is:
 1. A computer implemented method comprising:accessing sensor data collected for a roadway, the sensor data includingdata points having location information for objects; determining aninitial estimate for a location of an edge of a lane marker bounding alane; generating a set of possible estimates for the location of theedge based on an offset distance and the initial estimate; for eachestimate of the set of possible estimates, determining a likelihoodvalue indicative of how likely the estimate is to be the location of theedge; selecting a most likely estimate of the set of possible estimatesbased on the likelihood values; fitting, by a processor, the sensor datapoints to a model describing a location and shape of the edge; anddetermining a representation of the edge based on the model.
 2. Themethod of claim 1, wherein determining the initial estimate includesaccessing detailed map information for the roadway, and the initialestimate is based on a location of an edge included in the detailed mapinformation.
 3. The method of claim 1, wherein determining the initialestimate is based on a previous representation of the location of theedge of the lane marker.
 4. The method of claim 1, wherein generatingthe set of possible estimates for the location of the edge includesmoving the initial estimate laterally from the initial estimate theoffset distance a plurality of times.
 5. The method of claim 1, furthercomprising using the representation of the edge to maneuver a vehicleautonomously.
 6. The method of claim 1, further comprising: determiningan initial estimate for a location of a second edge of a second lanemarker bounding the lane; generating a second set of possible estimatesfor the location of the second edge based on the offset distance; foreach estimate of the second set of possible estimates, determining asecond likelihood value indicative of how likely the estimate is to bethe location of the second edge; selecting a second most likely estimateof the second set of possible estimates based on the likelihood values;filtering the sensor data to identify second data points within thepre-determined distance of the selected second most likely estimate;fitting the identified second data points to a second model describing asecond location and shape of the second edge; and determining a secondrepresentation of the second edge based on the second model.
 7. Themethod of claim 6, further comprising determining a centerline of thelane based on the representation and the second representation.
 8. Themethod of claim 6, further comprising maneuvering a vehicle autonomouslybased on the representation and the second representation.
 9. The methodof claim 1, further comprising receiving the sensor data from a laserdevice.
 10. The method of claim 1, further comprising: filtering thesensor data to identify data points within a pre-determined distance ofthe selected most likely estimate; wherein the sensor data points fit tothe model describing the location and shape of the edge are theidentified data points.
 11. A device comprising: memory storing sensordata collected for a roadway, the sensor data including data pointshaving location information for objects; a processor coupled to thememory, the processor being configured to: determine an initial estimatefor a location of an edge of a lane marker bounding a lane; generate aset of possible estimates for the location of the edge based on anoffset distance and the initial estimate; for each estimate of the setof possible estimates, determine a likelihood value indicative of howlikely the estimate is to be the location of the edge; select a mostlikely estimate of the set of possible estimates based on the likelihoodvalues; fit the sensor data points to a model describing a location andshape of the edge; and determine a representation of the edge based onthe model.
 12. The device of claim 11, wherein the processor isconfigured to determine the initial estimate by accessing detailed mapinformation for the roadway, and the initial estimate is based on alocation of an edge included in the detailed map information.
 13. Thedevice of claim 11, wherein the processor is configured to determine theinitial estimate based on a previous representation of the location ofthe edge of the lane marker.
 14. The device of claim 11, wherein theprocessor is configured to generate the set of possible estimates forthe location of the edge by moving the initial estimate laterally fromthe initial estimate the offset distance a plurality of times.
 15. Thedevice of claim 11, wherein the processor is configured to use therepresentation of the edge to maneuver a vehicle autonomously.
 16. Thedevice of claim 11, wherein the processor is further configured todetermine an initial estimate for a location of a second edge of asecond lane marker bounding the lane; generate a second set of possibleestimates for the location of the second edge based on the offsetdistance; for each estimate of the second set of possible estimates,determine a second likelihood value indicative of how likely theestimate is to be the location of the second edge; select a second mostlikely estimate of the second set of possible estimates based on thelikelihood values; filter the sensor data to identify second data pointswithin the pre-determined distance of the selected second most likelyestimate; fit the identified second data points to a second modeldescribing a second location and shape of the second edge; and determinea second representation of the second edge based on the second model.17. The device of claim 16, wherein the processor is further configuredto: determine a centerline of the lane based on the representation andthe second representation; and maneuver a vehicle along the centerline.18. The device of claim 16, wherein the processor is further configuredto maneuver a vehicle autonomously based on the representation and thesecond representation.
 19. The device of claim 11, wherein the processoris further configured to receive the sensor data from a laser device.20. The device of claim 11, wherein the processor is further configuredto: filter the sensor data to identify data points within apre-determined distance of the selected most likely estimate; whereinthe data points fit to the model describing the location and shape ofthe edge are the identified data points.
 21. A tangiblecomputer-readable storage medium on which computer readable instructionsof a program are stored, the instructions, when executed by a processor,cause the processor to perform a method, the method comprising:accessing sensor data collected for a roadway, the sensor data includingdata points having location information for objects; determining aninitial estimate for a location of an edge of a lane marker bounding alane; generating a set of possible estimates for the location of theedge based on an offset distance and the initial estimate; for eachestimate of the set of possible estimates, determining a likelihoodvalue indicative of how likely the estimate is to be the location of theedge; selecting a most likely estimate of the set of possible estimatesbased on the likelihood values; fitting the sensor data points to amodel describing a location and shape of the edge; and determining arepresentation of the edge based on the model.